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In order to solve the given traffic flow monitoring system existing the problems such as data collection dispersion, low vehicle identification, low in-time performance and big prediction error, a traffic flow prediction based on Internet of things and LSSVM (Least Squares Support Vector Machine) was proposed. Firstly, the principle and deployment model of system was described. Then the hardware of system includes vehicle sensor and Sink node was designed, and the system software was also introduced. The monitoring center executed the PCA method to extract the independent main information for the rude data, and then the LSSVM was operated to predict the traffic flow for the next time. Finally, the method in this paper was simulated in the environment of crossing road, and the simulation result shows:the proposed method can in time and accurately predict the traffic flow, and compared with other methods, it has the advantages of high fitting accuracy and generalizing ability. Therefore, it has big practicability.